corecting logic for selecting univariate diffuse filter and dealing
with correlated measurement errorstime-shift
Michel Juillard
parent
f0d1f033b0
commit
cfb5114d41
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@ -358,6 +358,7 @@ end
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diffuse_periods = 0;
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correlated_errors_have_been_checked = 0;
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switch DynareOptions.lik_init
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case 1% Standard initialization with the steady state of the state equation.
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if kalman_algo~=2
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@ -378,10 +379,14 @@ switch DynareOptions.lik_init
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a = zeros(mm,1);
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Zflag = 0;
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case 3% Diffuse Kalman filter (Durbin and Koopman)
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if kalman_algo ~= 4
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% Use standard kalman filter except if the univariate filter is explicitely choosen.
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if kalman_algo == 0
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kalman_algo = 3;
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elseif ~((kalman_algo == 3) || (kalman_algo == 4))
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error(['diffuse filter: options_.kalman_algo can only be equal ' ...
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'to 0 (default), 3 or 4'])
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end
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[Z,T,R,QT,Pstar,Pinf] = schur_statespace_transformation(Z,T,R,Q,DynareOptions.qz_criterium);
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Zflag = 1;
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% Run diffuse kalman filter on first periods.
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@ -403,38 +408,38 @@ switch DynareOptions.lik_init
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if isinf(dLIK)
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% Go to univariate diffuse filter if singularity problem.
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kalman_algo = 4;
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singularity_flag = 1;
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end
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end
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if (kalman_algo==4)
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% Univariate Diffuse Kalman Filter
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if singularity_flag
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if isequal(H,0)
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H = zeros(nobs,1);
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mmm = mm;
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if isequal(H,0)
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H = zeros(nobs,1);
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mmm = mm;
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else
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if all(all(abs(H-diag(diag(H)))<1e-14))% ie, the covariance matrix is diagonal...
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H = diag(H);
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mmm = mm;
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else
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if all(all(abs(H-diag(diag(H)))<1e-14))% ie, the covariance matrix is diagonal...
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H = diag(H);
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mmm = mm;
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else
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Z = [Z, eye(pp)];
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T = blkdiag(T,zeros(pp));
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Q = blkdiag(Q,H);
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R = blkdiag(R,eye(pp));
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Pstar = blkdiag(Pstar,H);
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Pinf = blckdiag(Pinf,zeros(pp));
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H = zeros(nobs,1);
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mmm = mm+pp;
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end
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Z = [Z, eye(pp)];
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T = blkdiag(T,zeros(pp));
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Q = blkdiag(Q,H);
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R = blkdiag(R,eye(pp));
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Pstar = blkdiag(Pstar,H);
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Pinf = blckdiag(Pinf,zeros(pp));
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H = zeros(nobs,1);
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mmm = mm+pp;
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end
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% no need to test again for correlation elements
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singularity_flag = 0;
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end
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[dLIK,tmp,a,Pstar] = univariate_kalman_filter_d(DynareDataset.missing.aindex,DynareDataset.missing.number_of_observations,DynareDataset.missing.no_more_missing_observations, ...
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Y, 1, size(Y,2), ...
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zeros(mmm,1), Pinf, Pstar, ...
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kalman_tol, riccati_tol, DynareOptions.presample, ...
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T,R,Q,H,Z,mmm,pp,rr);
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% no need to test again for correlation elements
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correlated_errors_have_been_checked = 1;
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[dLIK,tmp,a,Pstar] = univariate_kalman_filter_d(DynareDataset.missing.aindex,...
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DynareDataset.missing.number_of_observations,...
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DynareDataset.missing.no_more_missing_observations, ...
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Y, 1, size(Y,2), ...
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zeros(mmm,1), Pinf, Pstar, ...
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kalman_tol, riccati_tol, DynareOptions.presample, ...
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T,R,Q,H,Z,mmm,pp,rr);
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diffuse_periods = length(tmp);
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end
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case 4% Start from the solution of the Riccati equation.
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@ -605,7 +610,6 @@ if ((kalman_algo==1) || (kalman_algo==3))% Multivariate Kalman Filter
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else
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kalman_algo = 4;
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end
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singularity_flag = 1;
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else
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if DynareOptions.lik_init==3
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LIK = LIK + dLIK;
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@ -613,10 +617,10 @@ if ((kalman_algo==1) || (kalman_algo==3))% Multivariate Kalman Filter
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end
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end
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if ( singularity_flag || (kalman_algo==2) || (kalman_algo==4) )
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if (kalman_algo==2) || (kalman_algo==4)
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% Univariate Kalman Filter
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% resetting measurement error covariance matrix when necessary %
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if singularity_flag
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if ~correlated_errors_have_been_checked
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if isequal(H,0)
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H = zeros(nobs,1);
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mmm = mm;
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@ -254,6 +254,7 @@ end
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diffuse_periods = 0;
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correlated_errors_have_been_checked = 0;
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switch DynareOptions.lik_init
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case 1% Standard initialization with the steady state of the state equation.
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if kalman_algo~=2
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@ -274,10 +275,14 @@ switch DynareOptions.lik_init
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a = zeros(mm,1);
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Zflag = 0;
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case 3% Diffuse Kalman filter (Durbin and Koopman)
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if kalman_algo ~= 4
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% Use standard kalman filter except if the univariate filter is explicitely choosen.
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if kalman_algo == 0
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kalman_algo = 3;
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elseif ~((kalman_algo == 3) || (kalman_algo == 4))
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error(['diffuse filter: options_.kalman_algo can only be equal ' ...
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'to 0 (default), 3 or 4'])
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end
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[Z,T,R,QT,Pstar,Pinf] = schur_statespace_transformation(Z,T,R,Q,DynareOptions.qz_criterium);
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Zflag = 1;
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% Run diffuse kalman filter on first periods.
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@ -285,9 +290,9 @@ switch DynareOptions.lik_init
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% Multivariate Diffuse Kalman Filter
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if no_missing_data_flag
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[dLIK,dlik,a,Pstar] = kalman_filter_d(Y, 1, size(Y,2), ...
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zeros(mm,1), Pinf, Pstar, ...
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kalman_tol, riccati_tol, DynareOptions.presample, ...
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T,R,Q,H,Z,mm,pp,rr);
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zeros(mm,1), Pinf, Pstar, ...
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kalman_tol, riccati_tol, DynareOptions.presample, ...
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T,R,Q,H,Z,mm,pp,rr);
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else
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[dLIK,dlik,a,Pstar] = missing_observations_kalman_filter_d(DynareDataset.missing.aindex,DynareDataset.missing.number_of_observations,DynareDataset.missing.no_more_missing_observations, ...
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Y, 1, size(Y,2), ...
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@ -304,28 +309,27 @@ switch DynareOptions.lik_init
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end
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if (kalman_algo==4)
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% Univariate Diffuse Kalman Filter
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if singularity_flag
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if isequal(H,0)
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H = zeros(nobs,1);
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mmm = mm;
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if isequal(H,0)
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H = zeros(nobs,1);
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mmm = mm;
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else
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if all(all(abs(H-diag(diag(H)))<1e-14))% ie, the covariance matrix is diagonal...
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H = diag(H);
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mmm = mm;
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else
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if all(all(abs(H-diag(diag(H)))<1e-14))% ie, the covariance matrix is diagonal...
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H = diag(H);
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mmm = mm;
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else
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Z = [Z, eye(pp)];
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T = blkdiag(T,zeros(pp));
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Q = blkdiag(Q,H);
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R = blkdiag(R,eye(pp));
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Pstar = blkdiag(Pstar,H);
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Pinf = blckdiag(Pinf,zeros(pp));
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H = zeros(nobs,1);
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mmm = mm+pp;
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end
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Z = [Z, eye(pp)];
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T = blkdiag(T,zeros(pp));
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Q = blkdiag(Q,H);
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R = blkdiag(R,eye(pp));
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Pstar = blkdiag(Pstar,H);
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Pinf = blckdiag(Pinf,zeros(pp));
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H = zeros(nobs,1);
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mmm = mm+pp;
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end
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% no need to test again for correlation elements
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singularity_flag = 0;
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end
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% no need to test again for correlation elements
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correlated_errors_have_been_checked = 1;
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[dLIK,dlik,a,Pstar] = univariate_kalman_filter_d(DynareDataset.missing.aindex,DynareDataset.missing.number_of_observations,DynareDataset.missing.no_more_missing_observations, ...
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Y, 1, size(Y,2), ...
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zeros(mmm,1), Pinf, Pstar, ...
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@ -385,10 +389,10 @@ if ((kalman_algo==1) || (kalman_algo==3))% Multivariate Kalman Filter
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end
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end
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if ( singularity_flag || (kalman_algo==2) || (kalman_algo==4) )
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if (kalman_algo==2) || (kalman_algo==4)
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% Univariate Kalman Filter
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% resetting measurement error covariance matrix when necessary %
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if singularity_flag
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if ~correlated_errors_have_been_checked
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if isequal(H,0)
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H = zeros(nobs,1);
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mmm = mm;
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